
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "54"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder


# Use environment variables to figure out path to the csvs and auto_metrics
antenna = os.environ["ANTENNA"]
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')

antenna = "168"
csv_folder = "/home/obs/src/H5C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H5C_Notebooks/auto_metrics_inspect"


display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))


import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna


# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, '*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')

Found 85 csvs in /home/obs/src/H5C_Notebooks/_rtp_summary_
Found 81 auto_metrics notebooks in /home/obs/src/H5C_Notebooks/auto_metrics_inspect


# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H5C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'


this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        antnum = row['Ant']
        if antnum != int(antenna):
            continue
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break


# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])


display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))


html_to_display = ''
for am_html in auto_metric_htmls:
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line: 
            break


HTML(html_to_display)

JDs	A Priori Status	Auto Metrics Flags	Dead Fraction in Ant Metrics (Jee)	Dead Fraction in Ant Metrics (Jnn)	Crossed Fraction in Ant Metrics	Flag Fraction Before Redcal	Flagged By Redcal chi^2 Fraction	ee Shape Modified Z-Score	nn Shape Modified Z-Score	ee Power Modified Z-Score	nn Power Modified Z-Score	ee Temporal Variability Modified Z-Score	nn Temporal Variability Modified Z-Score	ee Temporal Discontinuties Modified Z-Score	nn Temporal Discontinuties Modified Z-Score	Average Dead Ant Metric (Jee)	Average Dead Ant Metric (Jnn)	Average Crossed Ant Metric	Median chi^2 Per Antenna (Jee)	Median chi^2 Per Antenna (Jnn)
2459594	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	100.705944	101.863667	inf	inf	26.730989	28.913207	87.835213	72.995018	nan	nan	nan	0.000000	0.000000
2459593	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.039876	4.478792	15.700434	23.590697	7.836884	22.366012	4.701088	6.404613	0.6997	0.6828	0.3004	7.309773	6.254506
2459592	RF_maintenance	100.00%	0.00%	0.00%	0.00%	-	-	3.298798	3.846913	49.923802	57.508120	22.200843	10.846877	-0.721780	0.226002	0.7824	0.7913	0.3556	nan	nan
2459591	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.344336	6.937756	18.319946	27.961797	7.463235	19.455651	-1.347513	-1.922232	0.6602	0.6600	0.3861	4.539158	3.947270
2459590	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	360.404950	360.088372	inf	inf	32.149233	34.154669	243.097684	234.332137	nan	nan	nan	0.000000	0.000000
2459589	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	336.443580	336.331331	inf	inf	44.064714	45.309840	327.802453	324.294812	nan	nan	nan	0.000000	0.000000
2459588	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	214.904666	214.260505	inf	inf	21.466608	24.114422	19.776800	18.274693	nan	nan	nan	0.000000	0.000000
2459587	RF_maintenance	100.00%	-	-	-	100.00%	0.00%	4.682510	7.350044	26.427361	39.331548	1.347337	3.770879	-1.778252	-2.006366	nan	nan	nan	7.909081	8.456048
2459586	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.747176	2.835189	0.374394	3.447625	5.406164	6.936645	-0.920794	-1.266243	0.6615	0.6609	0.3701	4.937915	4.097292
2459585	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	34.790137	36.293061	20.905191	24.137316	6.744252	7.985460	0.543621	-1.036464	0.7343	0.7377	0.2900	0.000000	0.000000
2459584	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	13.366537	17.606706	28.418459	40.080907	7.534234	12.905888	49.947422	47.418754	0.6147	0.6189	0.3774	11.265064	3.436781
2459583	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.406541	6.427691	7.943521	11.779682	4.696318	13.807282	-1.024106	-1.523183	0.6571	0.6545	0.4019	5.933506	4.867360
2459582	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.839804	6.211805	8.322946	10.978748	7.490109	18.286637	-1.332918	-1.581424	0.6674	0.6643	0.3894	7.400451	6.009573
2459581	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.865333	8.734379	7.573191	11.816802	4.507830	14.081950	-1.108214	-1.059461	0.6646	0.6618	0.3996	4.944162	4.087394
2459580	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.889524	6.437122	8.605207	12.203145	5.236794	14.878743	-1.234908	-0.895816	0.6680	0.6653	0.3933	10.282117	7.491074
2459579	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.673200	6.436157	9.881861	13.884266	6.095718	16.637260	-1.824028	-1.806098	0.6604	0.6570	0.4015	14.995089	11.056179
2459578	RF_maintenance	100.00%	0.00%	0.00%	0.00%	-	-	4.776221	4.604534	28.361469	32.340742	15.076549	12.015639	7.318549	7.421131	0.7851	0.8033	0.3696	nan	nan
2459577	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.561430	8.178789	7.229803	10.883078	5.289901	15.552898	-1.480496	-1.413223	0.6651	0.6636	0.3904	9.490370	7.160662
2459576	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.721899	6.197732	5.519035	7.995658	5.394419	13.781802	-1.368319	-1.165008	0.6848	0.6821	0.3959	6.054463	4.803924
2459575	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.623417	5.003151	5.711336	8.489396	4.411013	10.410185	4.490916	5.273663	0.7456	0.7343	0.3568	9.340828	7.163404
2459574	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459573	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.738653	7.437920	11.680714	16.016845	8.253919	18.401081	-1.734303	-1.569448	0.6820	0.6793	0.3939	10.912280	6.287040
2459572	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.907905	6.506490	5.435054	7.993790	4.042795	10.979470	-2.222262	-2.455888	0.6937	0.6911	0.3829	10.995187	7.308781
2459571	RF_maintenance	100.00%	0.00%	0.00%	0.00%	-	-	4.149454	4.103761	29.653198	33.211482	56.564827	30.365537	8.645761	1.796219	0.7167	0.7286	0.3496	nan	nan
2459570	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459569	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459566	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.627340	6.691019	7.396182	11.091051	5.381062	13.344270	-0.840322	-1.651365	0.6846	0.6847	0.4068	5.853260	4.519443
2459565	RF_maintenance	0.00%	-	-	-	100.00%	0.00%	0.751415	0.751415	0.674491	0.674491	0.881824	0.881824	0.311291	0.311291	nan	nan	nan	0.000000	0.000000
2459564	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.331982	3.646966	0.100334	3.102180	-1.609078	-0.046599	-0.538953	-1.513228	0.6903	0.6873	0.4009	5.159713	4.495530
2459563	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.036223	7.240934	16.021087	23.319387	5.630609	13.890688	-0.519048	-1.702307	0.6932	0.6937	0.3918	8.292630	5.667146
2459562	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	-0.010243	2.056626	-0.923270	0.827314	-2.403189	-0.493645	-0.145366	-1.124217	0.6971	0.7002	0.3861	5.242897	4.565866
2459561	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459560	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.211789	2.293092	-0.822691	1.183988	-1.842920	0.089397	-0.483442	-1.770164	0.6900	0.6898	0.4078	5.153908	4.552933
2459559	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.821677	3.206758	-0.213889	1.709288	-1.689479	0.534353	-0.958626	-1.502552	0.6890	0.6939	0.3955	4.106806	3.815024
2459558	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.162910	7.390236	8.536635	12.128596	5.279775	13.839927	-0.916388	-1.926452	0.6944	0.6916	0.4023	16.305355	10.998496
2459557	RF_maintenance	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.8348	0.8361	0.2723	nan	nan
2459556	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459554	RF_maintenance	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6951	0.6955	0.4029	nan	nan
2459553	RF_maintenance	-	0.00%	0.00%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.6932	0.6929	0.4017	nan	nan
2459552	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.711454	10.192373	11.746201	17.444946	11.008820	22.506328	-0.206620	0.212591	0.6950	0.6908	0.4023	7.672479	5.491670
2459551	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.446445	2.564004	-0.621436	1.506021	-2.883497	0.244429	-0.891954	-1.849732	0.6889	0.6928	0.4038	4.296426	3.859297
2459550	RF_maintenance	-	28.01%	28.01%	0.00%	-	-	nan	nan	nan	nan	nan	nan	nan	nan	0.5580	0.5720	0.2473	nan	nan
2459549	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.920442	7.309071	8.568392	12.822560	5.424363	14.056319	-1.685668	-2.322412	0.6902	0.6919	0.4140	4.535831	3.894090
2459542	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.406869	7.478053	17.247737	25.541500	-0.154523	1.204364	-1.838909	-2.152285	0.7581	0.7548	0.3491	17.211383	13.972790
2459541	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459540	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459536	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	6.715375	11.833976	14.856692	21.422334	11.632283	21.045372	-2.789642	-4.322330	0.7052	0.7055	0.4439	0.000000	0.000000
2459535	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	1.021451	2.715767	0.462172	2.256234	-2.517085	1.716407	-1.374988	-1.708748	0.7780	0.7613	0.3760	0.847277	0.849978
2459534	RF_maintenance	100.00%	0.00%	2.68%	0.00%	100.00%	0.00%	0.476227	2.215627	-0.530410	0.919963	-0.926241	25.600035	0.227521	0.185444	0.7631	0.7144	0.4005	17.007684	16.112126
2459533	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	4.766710	8.081799	8.831642	12.946699	5.380346	12.918271	0.812574	-2.422392	0.6933	0.7006	0.4288	0.000000	0.000000
2459532	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	5.279205	9.383441	11.668081	16.661994	6.801773	16.225295	-1.127220	-2.394209	0.7026	0.7066	0.4211	33.419498	15.381635
2459530	RF_maintenance	0.00%	0.00%	0.00%	0.00%	100.00%	0.00%	0.258128	2.594388	-0.461194	1.231345	-2.805616	0.356628	-1.443113	-1.604309	0.7206	0.7191	0.4155	0.871660	0.863874
2459527	RF_maintenance	0.00%	-	-	-	100.00%	0.00%	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459524	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459523	RF_maintenance	100.00%	-	-	-	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459522	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	nan	nan	inf	inf	nan	nan	nan	nan	nan	nan	nan	0.000000	0.000000
2459513	RF_maintenance	100.00%	0.00%	0.00%	0.00%	-	-	4.925329	5.251196	28.063214	31.456903	13.241498	17.737782	3.571506	5.562909	0.8806	0.9162	0.2128	nan	nan
2459508	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	3.147445	4.308617	4.783629	6.025257	2.999362	4.914440	4.046084	7.330183	0.9147	0.9516	0.1821	0.000000	0.000000
2459505	RF_maintenance	100.00%	0.00%	0.00%	0.00%	100.00%	0.00%	2.405375	2.809498	4.399022	7.495974	0.106337	3.452205	-0.098029	-0.862267	0.8927	0.8641	0.1862	24.537039	22.255716
2459503	RF_maintenance	100.00%	100.00%	100.00%	0.00%	100.00%	0.00%	5.565464	6.806339	5.760044	8.770196	7.400847	14.661532	1.057522	-1.136762	0.0421	0.1024	0.0053	0.000000	0.000000

Antenna Season Report Notebook¶

Antenna 168 Report

Load relevant info from summary CSVs¶

Table 1: Per-Night RTP Summary Info For This Atenna¶

Antenna 168, Node 15:

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

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Load antenna metric spectra and waterfalls from `auto_metrics` notebooks.¶